1. Field of the Invention
The present invention relates to a dynamic information processing system and a method therefor and more particularly to a knowledge information processing art (artificial intelligence art) which is suited to scheduling or rescheduling on a scene where the situation changes dynamically. The present invention is applied, for example, to scheduling or rescheduling for preparing or changing a train schedule or trainman schedule or for operating elevator maintenance engineers.
2. Description of the Prior Art
As an art for inferring by using a so-called knowledge base, there has been an art indicated in U.S. Pat. No. 4,648,044 (Basic expert system tool, Technoledge, Ltd., Steven Hardy, Mar. 3, 1987).
This is an art for describing knowledge according to rules and inferring using it. However, since a basic procedure for attaining a goal, an adjustment means of adjusting a trouble thereof, and an integration means for them are all described as rules, they exist together. Therefore, when problems become complex, the number of rules to be executed becomes enormous, and the processing speed rapidly decreases as the number of rules increases, and the rules are not intertwined with each other, and the reliability reduces.
On the other hand, an information processing system for goal and strategy type cooperation inference knowledge processing is described in U.S. Pat. No. 5,299,287. This is an expert system which is suited to planning or scheduling of large-scale complex plans such as preparation of train schedules.
This system is provided with a multi-hierarchy network for goal and strategy which is called a goal strategy net. The goal indicates a data block or frame representing a problem to be solved or a status to be aimed at. Hereinafter, the frame, data frame, and object are all called a frame. The strategy indicates a data block or frame representing a policy and means for attaining the goal (goal attaining method and means). The goal and strategy constitutes a multi-hierarchy network. The strategy includes a strategy in which procedures and rules for attaining a goal actually are described and also a strategy for recursively partitioning and executing a goal to sub-goals or integrating the sub-goals.
The inference unit infers using such a goal strategy net. For example, to prepare a train schedule, a goal of most significant "preparation of train schedule" is activated. Then, the inference unit partitions and executes the goal using the goal strategy net and obtains a solution by connecting, adjusting, and integrating obtained partial solutions. A goal and strategy oriented cooperation inference method for representing knowledge for partitioning a complex and large scale problem and connecting, adjusting, and integrating obtained partial solutions as mentioned above as a goal-strategy net and for inferring using it is known.
According the aforementioned information processing system described in U.S. Pat. No. 5,299,287, the knowledge is represented by the goal strategy net, so that there are not the aforementioned problems in the art in U.S. Pat. No. 4,648,044. Namely, since the knowledge is hierarchized, there is not a problem imposed that intertwisting between the rules cannot be seen and the reliability as a knowledge base system is improved. Flexible and fast inference is also available.
Such a goal and strategy oriented cooperation inference method is useful in solving static problems. However, this method is an art for incorporating one of the most significant goals to be attained by a system at the time of generation of the system and for solving it. Namely, a problem which can be interpreted is determined and fixed when the system is generated and the number of such problems is only one. Therefore, when various statuses are changed in real time and a plurality of problems to be solved are generated successively, the above method cannot solve them easily.
For example, the above method is suited to preparation of a train schedule every year or season. However, when a train schedule is actually applied, various status changes such as train delays due to vehicle breakdowns or rain or snow falls in real time and a plurality of problems (goals) are caused successively. The aforementioned method cannot be used so as to solve problems which are caused dynamically like this.